VisDB network training.
To evaluate the contribution of individual components in our method, we perform ablation studies on the 3DPW dataset [1]}.
Table REF  shows the performance of VisDB mesh outputs with/without truncation modeling \({\mathcal {L}_{vis}}^{x,y}\) , occlusion modeling \({\mathcal {L}_{vis}}^{z}\) , depth ordering loss \(\mathcal {L}_{depth}\) , and dense UV correspondence loss \(\mathcal {L}_{uv}\) .
Without \({\mathcal {L}_{vis}}^{x,y}\) , \({\mathcal {L}_{vis}}^{z}\) , \(\mathcal {L}_{depth}\) , and \(\mathcal {L}_{uv}\) , the vertex error increases by 6.3mm, 3.1mm, 3.9mm, and 1.9mm, respectively.
These results show that both visibility modeling and depth ordering loss play a crucial role in VisDB training.
